Abstract:
Supply chain is the coordination and synchronization of the flow of resources in the network of suppliers, manufactures, distributors and customers. The flow passes through different entities which individually have their own performance measures and objectives. In recent years, Supply Chain Management has received an increased amount of interest both from researchers and in the industry. Many concepts for supply chain design and modeling have been presented with different focuses. Most of the classical production models assume that all goods produced by manufacturer are perfect quality. But, in practice, imperfect goods production is quite inevitable. So, the effects of these defective goods need more focus by the researchers as an essential concern in supply chain cost optimization. Defective goods have always been a part of the production process that affects the overall costs, time and quality which are the major logistics performance. This study is to develop a new model for defective goods supply chain cost optimization which has the goal of fulfilling and grasping some new research opportunities in the field of supply chain cost optimization. This work addresses different types of supply chain costs considering the rework in order to capture its dynamic behavior. The proposed model is formulated to optimize the total supply chain cost considering the defective items within the chain. The model is applied to two real-world supply chains with different value streams for proving its functionality. Genetic Algorithm has been chosen to apply in this model because of its usability, availability and high probability of reaching the global optimum. The results are compared with that of LINPROG in order to demonstrate the model equations and their behavior.A sensitivity analysis is used in this study to determine the robustness and consistency of the behavior of the model under different methods, variables or assumptions. The analyses of results provide strong indications that the methodology and model introduced in this study are capable to generate knowledge to support academic research and real-world decision making regarding supply chain. Proper required index, parameters, and variables have been introduced to add more flexibility to the model implementation. In addition, the service costs and the possibility of shortage of materials at the delivery stage have been taken into account in the model that makes the model more realistic. Different viable data and sources enable the assembly of the model and provide more transparency of the results.Although this model is tested by two companies, it can be extended in any other company. Thus, the applications of the model are not limited to specific stages or levels of the chain. Moreover it has become the platform for the researcher that can unearth some interesting scopes for further research in this specific area.
